Import libraries¶
In [1]:
import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
from pylab import figure, axes, pie, title, show
Import Calibration Images¶
Import calibration images and find object and image points
In [2]:
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('camera_cal/*.jpg')
# Step through the list and search for chessboard corners
for idx, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (9,6), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and Save images
cv2.drawChessboardCorners(img, (9,6), corners, ret)
write_name = './output_images/corners_found'+str(idx)+'.jpg'
cv2.imwrite(write_name, img)
# Display the last calibration images
plt.imshow(img)
Out[2]:
Calculate Distortion correction coefficients¶
In [3]:
import pickle
%matplotlib inline
# Test undistortion on an image
img = cv2.imread('camera_cal/calibration5.jpg')
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given object points and image points.
# These are global variables and will be used later in the pipeline
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
cv2.imwrite('camera_cal/calibration5_ud.jpg',dst)
# Save the camera calibration result for later use (we won't worry about rvecs / tvecs)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump( dist_pickle, open( "camera_cal/wide_dist_pickle.p", "wb" ) )
# Visualize undistortion
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(dst)
ax2.set_title('Undistorted Image', fontsize=30)
# save for use in writeup
plt.savefig('./output_images/chessboard_caliberated.jpg')
Helper functions to make life easier¶
In [4]:
# Picked ths code snipped from the first Project
# Returns a masked image contaning only the region of interest
def region_of_interest(img, vertices):
"""
Applies an image mask.
Only keeps the region of the image defined by the polygon
formed from `vertices`. The rest of the image is set to black.
"""
#defining a blank mask to start with
mask = np.zeros_like(img)
#defining a 3 channel or 1 channel color to fill the mask with depending on the input image
if len(img.shape) > 2:
channel_count = img.shape[2] # i.e. 3 or 4 depending on your image
ignore_mask_color = (255,) * channel_count
else:
ignore_mask_color = 255
#filling pixels inside the polygon defined by "vertices" with the fill color
cv2.fillPoly(mask, vertices, ignore_mask_color)
#returning the image only where mask pixels are nonzero
masked_image = cv2.bitwise_and(img, mask)
return masked_image
# Sobel with threshold
def abs_sobel_thresh(img, orient='x', thresh_min=0, thresh_max=255):
gray = img
if orient == 'x':
grad = cv2.Sobel(gray, cv2.CV_64F, 1, 0)
else:
grad = cv2.Sobel(gray, cv2.CV_64F, 0, 1)
abs_sobel = np.absolute(grad)
abs_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
sxbinary = np.zeros_like(abs_sobel)
sxbinary[(abs_sobel >= thresh_min) & (abs_sobel <= thresh_max)] = 1
return sxbinary
# Find average of numpy array given a list of numby arrays
def get_avg(arr):
return np.mean( np.array(arr), axis=0 )
Preprocessing and prespective transform¶
In [5]:
import matplotlib.image as mpimg
M = None
Minv = None
j = 0
# Performs Preprocessing and prespective transform
# Returns a Warped image and Invers prespective transform variable (Minv)
# Saves Images at each stage of processing to be used in writeup
def pre_prespective(image):
global j,ret, mtx, dist, rvecs, tvecs, M, Minv
#Correct for camera distortion
dst = cv2.undistort(image, mtx, dist, None, mtx)
# Use only V channel from HSV space (Hue, Saturation, Value)
# This is good at picking the yellow line in varying light conditions
v_chan = cv2.cvtColor(dst,cv2.COLOR_RGB2HSV)[:,:,2]
# Use sobel on value channel
# This is better at picking lines at the very end of region of interest
cv2_exp = abs_sobel_thresh(v_chan, orient='x', thresh_min=20, thresh_max=250)
# The blue channel in rgb is used to pick the dashed white line
b_chan=dst[:,:,2]
# Combine them into a binary image
combined = np.zeros_like(b_chan)
combined[cv2_exp ==1] = 1
combined[b_chan >183] = 1
combined[v_chan >213] = 1
# create a mask for region of interest
imshape = image.shape
vertices = np.array([[(0.140*imshape[1],imshape[0]*0.940),(imshape[1]*0.437,imshape[0]*0.611), (imshape[1]*0.625,imshape[0]*0.611), (imshape[1]*0.871,imshape[0]*0.940)]], dtype=np.int32)
masked_image = region_of_interest(combined,vertices)
# Calculate prespective transformation Matrix only once
if M is None:
src = np.array([[210,700],[1070,700],[690,450],[590,450]],np.float32)
dsts = np.array([[352,720],[928,720],[928,20],[352,20]],np.float32)
M = cv2.getPerspectiveTransform(src, dsts)
Minv = cv2.getPerspectiveTransform(dsts, src)
# warp the image to get birds eye view
warp =cv2.warpPerspective(masked_image, M, gray.shape[::-1], flags=cv2.INTER_LINEAR)
# Save one of every hunderdth frame at various processing steps
if j%100 == 0:
mpimg.imsave('./output_images/'+str(j)+'.jpg',image)
mpimg.imsave('./output_images/'+str(j)+'_undistorted.jpg',dst)
cv2.imwrite('./output_images/'+str(j)+'_v_channel.jpg',v_chan)
cv2.imwrite('./output_images/'+str(j)+'_x_sobel.jpg',cv2_exp*255)
cv2.imwrite('./output_images/'+str(j)+'_b_channel.jpg',b_chan)
cv2.imwrite('./output_images/'+str(j)+'_combined.png',combined*255)
cv2.imwrite('./output_images/'+str(j)+'_masked.jpg',masked_image*255)
cv2.imwrite('./output_images/'+str(j)+'_birds_eye.jpg',warp*255)
return warp,Minv
Fit a Polynomial with sliding window¶
In [6]:
# This will be used for the first frame when we need to find the probable location of the lanes
def get_fits(binary_warped):
global j
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),
(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),
(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
if j%100 ==0:
figure(1, figsize=(8, 6))
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
plt.savefig('./output_images/'+str(j)+ '_window.png')
plt.close()
return left_fitx,right_fitx,left_fit,right_fit,ploty
Fit a polynomial when the previous frame's left_fit & right_fit is known¶
In [7]:
# It's now much easier to find line pixels!
def get_fit_contineous(left_fit,right_fit,binary_warped):
global j
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy +
left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) +
left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy +
right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) +
right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
if j%100 == 0:
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin,
ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin,
ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
plt.savefig('./output_images/'+str(j)+ '_region.png')
plt.close()
return left_fitx,right_fitx,left_fit,right_fit, ploty
Radius of Curvature and Distance From Center¶
In [8]:
# Calculate and display Curvature and Distance From Center
def radii(leftx,rightx,ploty,image):
fontScale=2
font = cv2.FONT_HERSHEY_SIMPLEX
fontColor = (255, 255, 255)
ym_per_pix = 30/700 # meters per pixel in y dimension
xm_per_pix = 3.7/576 # meters per pixel in x dimension
y_eval = np.max(ploty)
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
average_curverad = (right_curverad+left_curverad)/2
# Calculate distance from center
image_center = image.shape[1]/2
lane_center = (leftx[-1] + rightx[-1]) /2
center_dist = (image_center - lane_center)* xm_per_pix
# Write Curvature and distance value to the image.
cv2.putText(image, 'Curvature: {:.0f} m'.format( average_curverad ), (50, 50), font, fontScale, fontColor, 2)
cv2.putText(image, 'Distance from center: {:f} m'.format(center_dist), (50, 120), font, fontScale, fontColor, 2)
return image
Draw the road¶
Draw the road on a blank image using the left and right lane points. Transform back to the original prespective and overlay it on original image.
In [9]:
# Create an image to draw the lines on
def draw_road(image,binary_warped,left_fitx,right_fitx,ploty,Minv):
global j
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
if j% 100 == 0:
cv2.imwrite('./output_images/'+str(j)+ '_unwarped_road.jpg',color_warp)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(image, 1, newwarp, 0.3, 0)
return result
Final Pipeline¶
In [22]:
# Global variables used to store past frame values
g_left_fit = []
g_right_fit = []
g_ploty = []
g_left_fitx = []
g_right_fitx= []
def clear_globals():
global g_left_fit,g_right_fit,g_left_fitx,g_right_fitx,g_ploty,j
g_left_fit = []
g_right_fit = []
g_ploty = []
g_left_fitx = []
g_right_fitx= []
j = 0
# Final Pipeline
def pipeline(image):
global g_left_fit,g_right_fit,g_left_fitx,g_right_fitx,g_ploty,j
# Preprocess and warp image
binary_warped,Minv = pre_prespective(image)
# Check if the global variables have been initialized
if len(g_left_fit) == 0:
# Use sliding window if its the first frame
left_fitx,right_fitx,left_fit,right_fit,ploty = get_fits(binary_warped)
else:
# Use old Left_fit and Right_fit to find the lanes in current frame
left_fitx,right_fitx,left_fit,right_fit, ploty = get_fit_contineous(g_left_fit[-1],g_right_fit[-1],binary_warped)
# Buffer size for averaging
if len(g_left_fit) >10:
#Pop if list have more elements than buffer size
g_left_fit.pop(0)
g_right_fit.pop(0)
g_ploty.pop(0)
g_left_fitx.pop(0)
g_right_fitx.pop(0)
# append current value to global lists
g_left_fit.append(left_fit)
g_right_fit.append(right_fit)
g_ploty.append(ploty)
g_left_fitx.append(left_fitx)
g_right_fitx.append(right_fitx)
# Get average value of the buffer length
left_fitx = get_avg(g_left_fitx)
right_fitx = get_avg(g_right_fitx)
left_fit = get_avg(g_left_fit)
right_fit = get_avg(g_right_fit)
ploty = get_avg(g_ploty)
# Plot road and radius with the averaged values
result = draw_road(image,binary_warped,left_fitx,right_fitx,ploty,Minv)
result = radii(left_fitx,right_fitx,ploty,result)
#save every 100th result for use in writeup
if j%100 ==0:
mpimg.imsave('./output_images/'+str(j)+'_result.jpg',result)
# increment the count
j+=1
return result
Testing Pipeline on Images¶
In [23]:
#test1
clear_globals()
test = mpimg.imread('./test_images/test1.jpg')
# This will triger the sliding window search
text_result = pipeline(test)
#set j to 0 otherwise nothing will be saved
j=0
# This will trigger the region search
text_result = pipeline(test)
stages=['.jpg','_undistorted.jpg','_b_channel.jpg','_v_channel.jpg','_x_sobel.jpg','_combined.png','_masked.jpg','_birds_eye.jpg',
'_window.png','_region.png','_unwarped_road.jpg']
titles=['Original','Distortion Corrected','b channel from RGB',
'V channel from HSV','x sobel with Threshold','Combination of B layer ,V layer and Sobel',
'Masked Region of Interest','Prespective changed to Birds eye',
'Sliding window for lane line',' Region search for lane line','Road on Birds eye Prespective',' Final Result']
fig=plt.figure(figsize=(15,40))
columns = 2
rows = 6
for i in range(1, columns*rows):
img = mpimg.imread('./output_images/0'+stages[i-1])
fig.add_subplot(rows, columns, i)
plt.imshow(img,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
# Add Result
i+=1
fig.add_subplot(rows, columns, i)
plt.imshow(text_result,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
plt.show()
In [24]:
#test2
clear_globals()
test = mpimg.imread('./test_images/test2.jpg')
# This will triger the sliding window search
text_result = pipeline(test)
#set j to 0 otherwise nothing will be saved
j=0
# This will trigger the region search
text_result = pipeline(test)
fig=plt.figure(figsize=(15,40))
for i in range(1, columns*rows):
img = mpimg.imread('./output_images/0'+stages[i-1])
fig.add_subplot(rows, columns, i)
plt.imshow(img,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
# Add Result
i+=1
fig.add_subplot(rows, columns, i)
plt.imshow(text_result,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
plt.show()
In [25]:
#test3
clear_globals()
test = mpimg.imread('./test_images/test3.jpg')
# This will triger the sliding window search
text_result = pipeline(test)
#set j to 0 otherwise nothing will be saved
j=0
# This will trigger the region search
text_result = pipeline(test)
fig=plt.figure(figsize=(15,40))
for i in range(1, columns*rows):
img = mpimg.imread('./output_images/0'+stages[i-1])
fig.add_subplot(rows, columns, i)
plt.imshow(img,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
# Add Result
i+=1
fig.add_subplot(rows, columns, i)
plt.imshow(text_result,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
plt.show()
In [26]:
#test4
clear_globals()
test = mpimg.imread('./test_images/test4.jpg')
# This will triger the sliding window search
text_result = pipeline(test)
#set j to 0 otherwise nothing will be saved
j=0
# This will trigger the region search
text_result = pipeline(test)
fig=plt.figure(figsize=(15,40))
for i in range(1, columns*rows):
img = mpimg.imread('./output_images/0'+stages[i-1])
fig.add_subplot(rows, columns, i)
plt.imshow(img,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
# Add Result
i+=1
fig.add_subplot(rows, columns, i)
plt.imshow(text_result,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
plt.show()
In [27]:
#test5
clear_globals()
test = mpimg.imread('./test_images/test5.jpg')
# This will triger the sliding window search
text_result = pipeline(test)
#set j to 0 otherwise nothing will be saved
j=0
# This will trigger the region search
text_result = pipeline(test)
fig=plt.figure(figsize=(15,40))
for i in range(1, columns*rows):
img = mpimg.imread('./output_images/0'+stages[i-1])
fig.add_subplot(rows, columns, i)
plt.imshow(img,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
# Add Result
i+=1
fig.add_subplot(rows, columns, i)
plt.imshow(text_result,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
plt.show()
In [28]:
#test6
clear_globals()
test = mpimg.imread('./test_images/test6.jpg')
# This will triger the sliding window search
text_result = pipeline(test)
#set j to 0 otherwise nothing will be saved
j=0
# This will trigger the region search
text_result = pipeline(test)
fig=plt.figure(figsize=(15,40))
for i in range(1, columns*rows):
img = mpimg.imread('./output_images/0'+stages[i-1])
fig.add_subplot(rows, columns, i)
plt.imshow(img,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
# Add Result
i+=1
fig.add_subplot(rows, columns, i)
plt.imshow(text_result,cmap='gray')
plt.title(titles[i-1])
plt.xticks([])
plt.yticks([])
plt.show()
In [29]:
from moviepy.editor import VideoFileClip
from IPython.display import HTML
Run Pipeline for the video¶
In [30]:
clear_globals()
video_output = 'project_video_processed.mp4'
clip = VideoFileClip("project_video.mp4")
#clip2 = clip.subclip(22,24)
video_clip = clip.fl_image(pipeline)
%time video_clip.write_videofile(video_output, audio=False)